提出了一种基于稀疏表示的核素能谱特征提取方法，其实质是将核素能谱在区分性最好的稀疏原子上进行投影。利用稀疏分解方法对核素能谱进行稀疏分解，提取分解系数向量作为表征核素的特征向量，通过模式识别分类方法建立分类模型实现核素识别。与传统稀疏分解方法的区别在于：在能谱稀疏分解过程中按照稀疏字典中的原子排列顺序顺次进行分解；其次，分解目的在于特征提取，即最终提取到的特征对不同核素具有可区分性，并不要求核素能谱的重构精度。在241Am,133Ba,60Co,137Cs,131I和152Eu共6种核素1200个能谱数据上进行了核素识别实验，7种不同分类算法的平均识别率达到91.71%，实验结果的统计分析表明，本文提出的特征提取方法识别准确率显著地高于两种传统核素能谱特征提取方法准确率。

A sparse representation based method for nuclide spectrum feature extraction is proposed. The essence of this method is to decompose the energy spectrum on the best distinguishable sparse atom. The sparse decomposition method is used to decompose the nuclide energy spectrum, and the decomposition coefficient vector is taken as the feature to represent the energy spectrum. The classification model is established by the pattern recognition algorithm to realize the nuclide identification. The main difference from the traditional sparse decomposition method is that we decompose the energy spectrum in accordance with the sparse atoms in the sequential order in sparse dictionary. In the experiments, 6 kinds of radionuclide including 241Am, 133Ba, 60Co, 137Cs, 131I and 152Eu, 1200 energy spectra are used and the average nuclide identification accuracy on 7 different pattern recognition algorithms is 91.71%. The results of statistical tests show that the proposed algorithm performs significantly better than two traditional nuclide spectrum feature extraction methods.